Previous few-shot GAN algorithms fail to work in this setting as no ground truth images from new dataset are provided by the user. To address the challenge, we utilize a cross-domain image translation network from images to sketches \({F}:{\mathcal {X}}\rightarrow {\mathcal {Y}}\) . This network can be trained using input-output pairs such as photos and their sketch version. Alternatively, it can be learned through unpaired learning [1]}, [2]}. Once the mapping network is pre-trained, we do not need the sketch-image ground truth pairs during our model creation.
